Studying Evolutionary Solution Adaption Using a Flexibility Benchmark Based on a Metal Cutting Process

TL;DR

This paper introduces a bio-inspired framework and benchmark for evaluating the adaptability of evolutionary algorithms in multi-objective manufacturing process optimization, demonstrating that solution transfer reduces evaluation costs.

Contribution

It proposes a new flexibility benchmark based on metal cutting processes and extends NSGA-II with variants to improve solution transfer efficiency in dynamic optimization tasks.

Findings

Solution transfer significantly reduces optimization evaluations.

Extended NSGA-II variants improve adaptability over standard methods.

Benchmark facilitates studying algorithm flexibility in manufacturing contexts.

Abstract

We consider optimizing for different production requirements from the viewpoint of a bio-inspired framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks, which also relates to dynamic evolutionary optimization. Optimizing manufacturing process parameters is typically a multi-objective problem with often contradictory objectives such as production quality and production time. If production requirements change, process parameters have to be optimized again. Since optimization usually requires costly simulations based on, for example, the Finite Element method, it is of great interest to have means to reduce the number of evaluations needed for optimization. Based on the extended Oxley model for orthogonal metal cutting, we introduce a multi-objective optimization benchmark where different materials define related optimization tasks. We use the benchmark to study the flexibility of NSGA-II, which we extend by two variants: 1) varying goals, which optimizes solutions for two tasks simultaneously to obtain in-between source solutions expected to be more adaptable, and 2) active-inactive genotype, which accommodates different possibilities that can be activated or deactivated. Results show that adaption, i.e. transferring a solution from a previous optimization task, with standard NSGA-II greatly reduces the number of evaluations required for optimization to a target goal in comparison to starting from scratch. The proposed variants further improve the adaption costs, although further work is needed towards making the methods advantageous for real applications.